






"""simple docstring"""





import inspect
import os
import unittest
from pathlib import Path

import torch

import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command






class 	a		(   unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	List[str]		=					inspect.getfile(accelerate.test_utils						)
    SCREAMING_SNAKE_CASE		:	List[str]		=					os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ["""scripts""", """test_cli.py"""]						)

    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					["""accelerate""", """launch"""]
    SCREAMING_SNAKE_CASE		:	str		=					Path.home() / """.cache/huggingface/accelerate"""
    SCREAMING_SNAKE_CASE		:	Any		=					"""default_config.yaml"""
    SCREAMING_SNAKE_CASE		:	Any		=					config_folder / config_file
    SCREAMING_SNAKE_CASE		:	str		=					config_folder / """_default_config.yaml"""

    SCREAMING_SNAKE_CASE		:	Optional[int]		=					Path("""tests/test_configs"""						)






    @classmethod
    def   UpperCamelCase			(			cls	:				Optional[Any]   )	->						Dict:
      if cls.config_path.is_file():
        cls.config_path.rename(cls.changed_path   )






    @classmethod
    def   UpperCamelCase			(			cls	:				Tuple   )	->						Union[str, Any]:
      if cls.changed_path.is_file():
        cls.changed_path.rename(cls.config_path   )






    def   UpperCamelCase			(			self	:				List[str]   )	->						List[str]:
      lowerCamelCase_       	=		self.base_cmd
      if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
        cmd += ["--multi_gpu"]
      execute_subprocess_async(cmd + [self.test_file_path]			,		env=os.environ.copy()   )






    def   UpperCamelCase			(			self	:				Dict   )	->						Tuple:
      for config in sorted(self.test_config_path.glob('**/*.yaml'   )   ):
        with self.subTest(config_file=__SCREAMING_SNAKE_CASE   ):
          execute_subprocess_async(
              self.base_cmd + ['--config_file', str(__SCREAMING_SNAKE_CASE   ), self.test_file_path]			,		env=os.environ.copy()   )






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						str:
      execute_subprocess_async(['accelerate', 'test']			,		env=os.environ.copy()   )






class 	a		(   unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	List[str]		=					"""test-tpu"""
    SCREAMING_SNAKE_CASE		:	Any		=					"""us-central1-a"""
    SCREAMING_SNAKE_CASE		:	Tuple		=					"""ls"""
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					["""accelerate""", """tpu-config"""]
    SCREAMING_SNAKE_CASE		:	List[str]		=					"""cd /usr/share"""
    SCREAMING_SNAKE_CASE		:	str		=					"""tests/test_samples/test_command_file.sh"""
    SCREAMING_SNAKE_CASE		:	Any		=					"""Running gcloud compute tpus tpu-vm ssh"""






    def   UpperCamelCase			(			self	:				str   )	->						List[str]:
      lowerCamelCase_       	=		run_command(
          self.cmd
          + ['--command', self.command, '--tpu_zone', self.tpu_zone, '--tpu_name', self.tpu_name, '--debug']			,		return_stdout=__SCREAMING_SNAKE_CASE			,		)
      self.assertIn(
          F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'''			,		__SCREAMING_SNAKE_CASE			,		)






    def   UpperCamelCase			(			self	:				Any   )	->						List[str]:
      lowerCamelCase_       	=		run_command(
          self.cmd
          + [
              '--config_file',
              'tests/test_configs/0_12_0.yaml',
              '--command',
              self.command,
              '--tpu_zone',
              self.tpu_zone,
              '--tpu_name',
              self.tpu_name,
              '--debug',
          ]			,		return_stdout=__SCREAMING_SNAKE_CASE			,		)
      self.assertIn(
          F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'''			,		__SCREAMING_SNAKE_CASE			,		)






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						int:
      lowerCamelCase_       	=		run_command(
          self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--debug']			,		return_stdout=__SCREAMING_SNAKE_CASE   )
      self.assertIn(
          F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all'''			,		__SCREAMING_SNAKE_CASE			,		)






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						int:
      lowerCamelCase_       	=		run_command(
          self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--command', self.command, '--debug']			,		return_stdout=__SCREAMING_SNAKE_CASE			,		)
      self.assertIn(
          F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'''			,		__SCREAMING_SNAKE_CASE			,		)






    def   UpperCamelCase			(			self	:				List[Any]   )	->						List[str]:
      lowerCamelCase_       	=		run_command(
          self.cmd
          + [
              '--config_file',
              'tests/test_configs/latest.yaml',
              '--command',
              self.command,
              '--command',
              'echo "Hello World"',
              '--debug',
          ]			,		return_stdout=__SCREAMING_SNAKE_CASE			,		)
      self.assertIn(
          F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all'''			,		__SCREAMING_SNAKE_CASE			,		)






    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Dict:
      lowerCamelCase_       	=		run_command(
          self.cmd
          + ['--config_file', 'tests/test_configs/latest.yaml', '--command_file', self.command_file, '--debug']			,		return_stdout=__SCREAMING_SNAKE_CASE			,		)
      self.assertIn(
          F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all'''			,		__SCREAMING_SNAKE_CASE			,		)






    def   UpperCamelCase			(			self	:				Tuple   )	->						Tuple:
      lowerCamelCase_       	=		run_command(
          self.cmd
          + [
              '--config_file',
              'tests/test_configs/0_12_0.yaml',
              '--command_file',
              self.command_file,
              '--tpu_zone',
              self.tpu_zone,
              '--tpu_name',
              self.tpu_name,
              '--debug',
          ]			,		return_stdout=__SCREAMING_SNAKE_CASE			,		)
      self.assertIn(
          F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all'''			,		__SCREAMING_SNAKE_CASE			,		)






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Union[str, Any]:
      lowerCamelCase_       	=		run_command(
          self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--install_accelerate', '--debug']			,		return_stdout=__SCREAMING_SNAKE_CASE			,		)
      self.assertIn(
          F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo "hello world"; echo "this is a second command" --worker all'''			,		__SCREAMING_SNAKE_CASE			,		)






    def   UpperCamelCase			(			self	:				str   )	->						List[Any]:
      lowerCamelCase_       	=		run_command(
          self.cmd
          + [
              '--config_file',
              'tests/test_configs/latest.yaml',
              '--install_accelerate',
              '--accelerate_version',
              '12.0.0',
              '--debug',
          ]			,		return_stdout=__SCREAMING_SNAKE_CASE			,		)
      self.assertIn(
          F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo "hello world"; echo "this is a second command" --worker all'''			,		__SCREAMING_SNAKE_CASE			,		)












"""simple docstring"""





from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin






@dataclass
class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	torch.FloatTensor






class 	a		(   __snake_case    , __snake_case						):






    @register_to_config
    def __init__(			self	:				Any			,		__SCREAMING_SNAKE_CASE	:				int = 32			,		__SCREAMING_SNAKE_CASE	:				int = 64			,		__SCREAMING_SNAKE_CASE	:				int = 20			,		__SCREAMING_SNAKE_CASE	:				int = 768			,		__SCREAMING_SNAKE_CASE	:				int=77			,		__SCREAMING_SNAKE_CASE	:				Any=4			,		__SCREAMING_SNAKE_CASE	:				float = 0.0			,		__SCREAMING_SNAKE_CASE	:				str = "silu"			,		__SCREAMING_SNAKE_CASE	:				Optional[str] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[str] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[str] = "linear"			,		__SCREAMING_SNAKE_CASE	:				Optional[str] = "prd"			,		__SCREAMING_SNAKE_CASE	:				Optional[int] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[int] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[int] = None			,		)	->						List[Any]:
      super().__init__()
      lowerCamelCase_       	=		num_attention_heads
      lowerCamelCase_       	=		attention_head_dim
      lowerCamelCase_       	=		num_attention_heads * attention_head_dim
      lowerCamelCase_       	=		additional_embeddings

      lowerCamelCase_       	=		time_embed_dim or inner_dim
      lowerCamelCase_       	=		embedding_proj_dim or embedding_dim
      lowerCamelCase_       	=		clip_embed_dim or embedding_dim

      lowerCamelCase_       	=		Timesteps(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		0   )
      lowerCamelCase_       	=		TimestepEmbedding(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		out_dim=__SCREAMING_SNAKE_CASE			,		act_fn=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		nn.Linear(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      if embedding_proj_norm_type is None:
        lowerCamelCase_       	=		None
      elif embedding_proj_norm_type == "layer":
        lowerCamelCase_       	=		nn.LayerNorm(__SCREAMING_SNAKE_CASE   )
      else:
        raise ValueError(F'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'''   )

      lowerCamelCase_       	=		nn.Linear(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      if encoder_hid_proj_type is None:
        lowerCamelCase_       	=		None
      elif encoder_hid_proj_type == "linear":
        lowerCamelCase_       	=		nn.Linear(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
      else:
        raise ValueError(F'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'''   )

      lowerCamelCase_       	=		nn.Parameter(torch.zeros(1			,		num_embeddings + additional_embeddings			,		__SCREAMING_SNAKE_CASE   )   )

      if added_emb_type == "prd":
        lowerCamelCase_       	=		nn.Parameter(torch.zeros(1			,		1			,		__SCREAMING_SNAKE_CASE   )   )
      elif added_emb_type is None:
        lowerCamelCase_       	=		None
      else:
        raise ValueError(
            F'''`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.'''   )

      lowerCamelCase_       	=		nn.ModuleList(
          [
              BasicTransformerBlock(
                  __SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		dropout=__SCREAMING_SNAKE_CASE			,		activation_fn='gelu'			,		attention_bias=__SCREAMING_SNAKE_CASE			,		)
              for d in range(__SCREAMING_SNAKE_CASE   )
          ]   )

      if norm_in_type == "layer":
        lowerCamelCase_       	=		nn.LayerNorm(__SCREAMING_SNAKE_CASE   )
      elif norm_in_type is None:
        lowerCamelCase_       	=		None
      else:
        raise ValueError(F'''Unsupported norm_in_type: {norm_in_type}.'''   )

      lowerCamelCase_       	=		nn.LayerNorm(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		nn.Linear(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		torch.full(
          [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]			,		-10_000.0   )
      causal_attention_mask.triu_(1   )
      lowerCamelCase_       	=		causal_attention_mask[None, ...]
      self.register_buffer('causal_attention_mask'			,		__SCREAMING_SNAKE_CASE			,		persistent=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		nn.Parameter(torch.zeros(1			,		__SCREAMING_SNAKE_CASE   )   )
      lowerCamelCase_       	=		nn.Parameter(torch.zeros(1			,		__SCREAMING_SNAKE_CASE   )   )






    @property
    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
    def   UpperCamelCase			(			self	:				List[str]   )	->						Dict[str, AttentionProcessor]:
      lowerCamelCase_       	=		{}

      def fn_recursive_add_processors(__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				torch.nn.Module			,		__SCREAMING_SNAKE_CASE	:				Dict[str, AttentionProcessor]   ):
        if hasattr(__SCREAMING_SNAKE_CASE			,		'set_processor'   ):
          lowerCamelCase_       	=		module.processor

        for sub_name, child in module.named_children():
          fn_recursive_add_processors(F'''{name}.{sub_name}'''			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

        return processors

      for name, module in self.named_children():
        fn_recursive_add_processors(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      return processors






    def   UpperCamelCase			(			self	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Union[AttentionProcessor, Dict[str, AttentionProcessor]]   )	->						Optional[int]:
      lowerCamelCase_       	=		len(self.attn_processors.keys()   )

      if isinstance(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ) and len(__SCREAMING_SNAKE_CASE   ) != count:
        raise ValueError(
            F'''A dict of processors was passed, but the number of processors {len(__SCREAMING_SNAKE_CASE   )} does not match the'''
            F''' number of attention layers: {count}. Please make sure to pass {count} processor classes.'''   )

      def fn_recursive_attn_processor(__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				torch.nn.Module			,		__SCREAMING_SNAKE_CASE	:				str   ):
        if hasattr(__SCREAMING_SNAKE_CASE			,		'set_processor'   ):
          if not isinstance(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
            module.set_processor(__SCREAMING_SNAKE_CASE   )
          else:
            module.set_processor(processor.pop(F'''{name}.processor'''   )   )

        for sub_name, child in module.named_children():
          fn_recursive_attn_processor(F'''{name}.{sub_name}'''			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      for name, module in self.named_children():
        fn_recursive_attn_processor(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Any   )	->						int:
      self.set_attn_processor(AttnProcessor()   )






    def   UpperCamelCase			(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				Dict			,		__SCREAMING_SNAKE_CASE	:				Union[torch.Tensor, float, int]			,		__SCREAMING_SNAKE_CASE	:				torch.FloatTensor			,		__SCREAMING_SNAKE_CASE	:				Optional[torch.FloatTensor] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[torch.BoolTensor] = None			,		__SCREAMING_SNAKE_CASE	:				bool = True			,		)	->						List[Any]:
      lowerCamelCase_       	=		hidden_states.shape[0]

      lowerCamelCase_       	=		timestep
      if not torch.is_tensor(__SCREAMING_SNAKE_CASE   ):
        lowerCamelCase_       	=		torch.tensor([timesteps]			,		dtype=torch.long			,		device=hidden_states.device   )
      elif torch.is_tensor(__SCREAMING_SNAKE_CASE   ) and len(timesteps.shape   ) == 0:
        lowerCamelCase_       	=		timesteps[None].to(hidden_states.device   )

      # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
      lowerCamelCase_       	=		timesteps * torch.ones(__SCREAMING_SNAKE_CASE			,		dtype=timesteps.dtype			,		device=timesteps.device   )

      lowerCamelCase_       	=		self.time_proj(__SCREAMING_SNAKE_CASE   )

      # timesteps does not contain any weights and will always return f32 tensors
      # but time_embedding might be fp16, so we need to cast here.
      lowerCamelCase_       	=		timesteps_projected.to(dtype=self.dtype   )
      lowerCamelCase_       	=		self.time_embedding(__SCREAMING_SNAKE_CASE   )

      if self.embedding_proj_norm is not None:
        lowerCamelCase_       	=		self.embedding_proj_norm(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		self.embedding_proj(__SCREAMING_SNAKE_CASE   )
      if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
        lowerCamelCase_       	=		self.encoder_hidden_states_proj(__SCREAMING_SNAKE_CASE   )
      elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
        raise ValueError('`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set'   )

      lowerCamelCase_       	=		self.proj_in(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		self.positional_embedding.to(hidden_states.dtype   )

      lowerCamelCase_       	=		[]
      lowerCamelCase_       	=		0

      if encoder_hidden_states is not None:
        additional_embeds.append(__SCREAMING_SNAKE_CASE   )
        additional_embeddings_len += encoder_hidden_states.shape[1]

      if len(proj_embeddings.shape   ) == 2:
        lowerCamelCase_       	=		proj_embeddings[:, None, :]

      if len(hidden_states.shape   ) == 2:
        lowerCamelCase_       	=		hidden_states[:, None, :]

      lowerCamelCase_       	=		additional_embeds + [
          proj_embeddings,
          time_embeddings[:, None, :],
          hidden_states,
      ]

      if self.prd_embedding is not None:
        lowerCamelCase_       	=		self.prd_embedding.to(hidden_states.dtype   ).expand(__SCREAMING_SNAKE_CASE			,		-1			,		-1   )
        additional_embeds.append(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		torch.cat(
          __SCREAMING_SNAKE_CASE			,		dim=1			,		)

      # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
      lowerCamelCase_       	=		additional_embeddings_len + proj_embeddings.shape[1] + 1
      if positional_embeddings.shape[1] < hidden_states.shape[1]:
        lowerCamelCase_       	=		F.pad(
            __SCREAMING_SNAKE_CASE			,		(
                0,
                0,
                additional_embeddings_len,
                self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
            )			,		value=0.0			,		)

      lowerCamelCase_       	=		hidden_states + positional_embeddings

      if attention_mask is not None:
        lowerCamelCase_       	=		(1 - attention_mask.to(hidden_states.dtype   )) * -10_000.0
        lowerCamelCase_       	=		F.pad(__SCREAMING_SNAKE_CASE			,		(0, self.additional_embeddings)			,		value=0.0   )
        lowerCamelCase_       	=		(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype   )
        lowerCamelCase_       	=		attention_mask.repeat_interleave(self.config.num_attention_heads			,		dim=0   )

      if self.norm_in is not None:
        lowerCamelCase_       	=		self.norm_in(__SCREAMING_SNAKE_CASE   )

      for block in self.transformer_blocks:
        lowerCamelCase_       	=		block(__SCREAMING_SNAKE_CASE			,		attention_mask=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		self.norm_out(__SCREAMING_SNAKE_CASE   )

      if self.prd_embedding is not None:
        lowerCamelCase_       	=		hidden_states[:, -1]
      else:
        lowerCamelCase_       	=		hidden_states[:, additional_embeddings_len:]

      lowerCamelCase_       	=		self.proj_to_clip_embeddings(__SCREAMING_SNAKE_CASE   )

      if not return_dict:
        return (predicted_image_embedding,)

      return PriorTransformerOutput(predicted_image_embedding=__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[int]   )	->						Optional[int]:
      lowerCamelCase_       	=		(prior_latents * self.clip_std) + self.clip_mean
      return prior_latents




